"GeoDec"

and
simulation of a geographical location, rapidly
and accurately.

GeoDec (Geospatial
Decision Making) is a collaborative project withDr. Cyrus
Shahabi,Dr. Craig
Knoblock ,Dr. Ulrich
Neumann and Dr. Ramakant
Nevatiaunder IMSC to build an information-rich and realisticgeospatial space (e.g., a city) with temporal dimension rapidly and accurately, which supports visualization, querying and data analysis capabilities. Recent growth of the geo-spatial
information and their availability has motivated the effort to
integrate them to support a comprehensive set of queries in
different modalities. However due to the inherent difference in data
formats available and their different accuracy levels, doing a
seamless, consistent and efficient integration of different data
sources is a very challenging task.

By
utilizing various information integration
approaches such as orthoimagery and street maps
conflation, vector data and satellite imagery
conflation and road network and map fusion, we
strive to create intelligent, information-rich
and detailed models that incorporate the visual
appeal and accuracy of imagery with detailed
attribution information in diverse maps and
realistic 3-D visualization for geographic
locations.

This
interdisciplinary work is being performed by
researches from various fields such as
databases, artificial intelligence, computer
graphics and computer vision in a collaborative
effort as part of IMSC, the only NSF engineering research
center in the area of Multimedia for integration
of various technologies under the GeoDec
framework.

Our proposed framework is composed of the
following components :

Rapid 3-D model construction from
photographs:

This system is designed to
minimize users' interaction by
giving them the option of working
with a single image at a time. At
the same time, by using basic
information such as one side and
one corner of objects identified
by user in a semi-automatic
process, highly complex polygonal
roof shaped buildings can be
modeled.

Here is 3D models of USC campus with photograph texture rendered in VRML:

The 3D models of Downtown Los Angeles

The 3D models of ShangHai Jiao Tong University, China

Texture mapping of buildings and
video fusion:

In
a two fold approach, static and
dynamics of scenes are captured
from a photographic process
involving video or numerous still
images. Static textures capture
the color attributes of buildings
and street scenes and then
enhanced with video streams to
show dynamic aspects of scenes
(e.g. movement of cars on other
objects mapped on the 3-D model
(video fusion).

The next step
is to enhance the model by integrating all sorts
of data from both online sources (e.g., yellow
pages, property-tax sites, geocoders) as well as
private databases (e.g., road vector data, maps,
gazetteer points).

Integrating Vector Data and Imagery:

This research is on the problem
of accurate integration of geospatial vector data with (satellite or aerial)
images. One application for such integration could be for the purpose of
automatic recognition and annotation of spatial objects in imagery. We utilized
a wide variety of geospatial and textual data available on the Internet in
order to efficiently and accurately identify objects in the satellite imagery.
To demonstrate the utility of our technique, we built an application that
utilizes the satellite imagery from the Microsoft TerraService and the
Tigerline vector files from US Census Bureau (as well as some online sources)
to annotate buildings on the imagery. Our main challenge is that geospatial
data (specifically, vector and image data) obtained from various data sources
may have different projections, different accuracy levels, and different
inconsistencies. The applications that integrate information from various
geospatial data sources must be able to overcome these inconsistencies
accurately, in real-time and for large regions. Traditionally, this problem has
been in the domain of the image processing and GIS systems. However, the
conflation approach used in various GIS systems to manually or
semi-automatically align two geospatial data sets does not scale up to large
regions.
We utilize a fully automatic
conflation technique to fix the
vector data and image alignment.

Based on the above approach, we
use common vector datasets as
"glue" to further integrate
imagery with maps. This is done by
having a set of control point
pairs for the map and imagery,
using conventional conflation
technique to align the map with
the satellite imagery. Please refer to Geosemble Technologies for more details.

Effective Presentation and Querying:

We are currently developing
a user interface -
Negaah- (Negaah in Persian
means “viewing”) for GeoDec
that will allow the user to
navigate and interactively
query the 3D environment in
real-time. Negaah allows
greater decision-making
flexibility by allowing the
user to query geospatial
data based on a user-defined
selection area. Negaah will
also allow the user to
submit queries based on a
different time intervals for
temporal data (such as
object trajectories or
stored video streams). The
users can selectively query
and display different layers
of information. Negaah also
supports more sophisticated
queries such as calculating
the shortest path between
two points.

All the queries in Negaah
are directed to GeoDec’s
information mediator/spatio-temporal
database component through a
new middleware layer, Jooya
(Jooya in Persian means
“finder”). Jooya offers a
universal way of specifying
the type of query, as well
as its parameters by a GUI
and retrieves the results
back in a unified way
(currently using the KML
format). Jooya queries not
only the information
mediator but also our
private spatial database for
vector data, moving objects
and 3d building models as
well as the video-server. It
is a thin layer between GUI
and
mediator/database/specialized-servers
to unify query as well as
query results.
Therefore any visualization
layer can sit on top of
Jooya for its integrated
query and access needs.
Currently, we could port
Negaah, Microsoft Virtual Earth and Google Earth
on top of Jooya. This design
makes the interface
independent of the inherent
data model and facilitates
scaling the architecture by
allowing several
visualization components to
specify queries and receive
the results back in a
uniform language hiding the
source of information.

Point data (eg. text information concerning a query point, like building names);

Point of interests (eg. Restaurants, Named Place, Parkings, Hospitals ...);

Road vector data;

Traffic information;

Moving
objects/trajectories (such
as trams);

Parcel Information;

Video;

Event (e.g Query about videos concering a given event);

Visibility;

Nearest neighbors;

The following example shows combined queries of "3D buildings" and "Point of Interests":

Video Query:
In addition to images
(maps), alphanumeric and
geospatial vector data, we
are integrating numerous
video streams acquired from
video sensors that are
accessible within the GeoDec
environment. We provide the
functionality to search for
the videos within a given
area of interest and during
a given time interval. The
video sources can either be
spatio-temporaly indexed
videos stored in media
servers or live videos
acquired from live cameras.
The video clips satisfying
the query conditions are
streamed and displayed to
user. Our media streaming
and storage architecture is
specifically designed to
scale to a large number of
concurrent streams. Users
might simultaneously view
several live and
pre-recorded videos in a
geospatial region and go
back in time by navigating
through a temporal history
browser implemented in
Negaah. We have enabled this
functionality by
continuously recording a
sliding temporal window of
all the incoming video
streams.

Visibility Query:

The visibility query color-codes all the areas (including gournd and building facades) that is visible to/from to a query point within a certain range.

Blue: Building facades and grounds that are visible to the query point.

Glove-Based User
InterfaceWe have developed a glove-based user interface for
users to navigate these immersive and information-rich
three-dimensional environments.Our system interprets user
commands based on hand gestures obtained using data gloves and an
extended range tracking device. This interface allows the user to
navigate and interact with the environment intuitively, particularly
when using a large display.In addition, automatic level of
detail control displays point data (e.g. building names) on the
model when it is contextually relevant to the user, based on the
distance of the user from the object. The user can also use the
glove-based user interface to view other information integrated with
the model such as conflated vector or image data.

Challenges:

Realistic rendering

Accurate information fusion

Interactive query and access

Scalable infrastructure

Efficient in time-to-build

Applications:

City Planners

Military intelligence

Simulation & training

Computer game

Real-estate

News broadcast

Future work:

As
part of our future work we would
like to achieve the following
goals: